Data trends at meteoLCD: 1998 to 2023
Trends computed from yearly averages at meteoLCD,
Diekirch, Luxembourg.
Graphs may be freely copied and used, under the condition to cite:
MASSEN, Francis: Data trends at meteoLCD, 1998 to 2023. https://meteo.lcd.lu
Older trends are here!
Attention: in all trend equations (y = a+b*x) the variable x represents the year, with x = 1 for the first year in the trend period (usually 1998 or 2002).
Most important conclusions from 1998 (2002) to 2023:
1. Overall increase in sunshine duration since 1998 by
19
hours*decade^{1} and even 52
hours*decade^{1 } since 2002.
2. Local temperatures show warming of 0.011°C/y since 2002
(2018 was a strong El Nino year), a spectacular cooling during 2021
followed by strong warming in 20222023:
This warming is compatible with the positive
trend in yearly sunshine hours.
3. Diurnal temperature range (DTR) trend since 1998 is positive (= no
anthropogenic warming fingerprint ).
4. The winter trend since 2002 shows a warming of +1.1 °C*decade^{1 }
; this positive trend is also shown by the winter NAO index (+0.3 decade^{1})
5. Since 1998 the ground O3 trend is positive (2022 reading is suspicious)
6. Local CO2 mixing ratio continues to increase at about 4.1 ppmV per
year; the asymptotic background is ca. 5 ppm higher to that measured at Mauna Loa
(latitude adjusted by + 1.8 ppmV).
7. The trend of the biologically effective yearly UVB dose is distinctly
positive from 1998^{
}
to 2023^{
}
(like solar energy and UVA dose); note visible decrease in all solar
parameters in 2021.^{
}
8. The trend of the UVA dose is distinctly positive from 1998 to 2023
9. Precipitation (rainfall) shows a sinusoidal pattern of period ~5.4
years (65 months). Linear trend from from 2003 to 2023 is nearly flat
(+0.87mm/year).
10. Energy content of moist air (enthalpy) shows a positive trend since
2002
(compatible with warming trend)
11. The fine particles PM2.5 concentrations are low,
and
are well synchronized with those at Beidweiler.
12. There is a very slight negative trend in the total ozone column (TOC) since
2002, the last 8 years have again a positive trend. The synchronicity with the
Uccle TOC measurements
is excellent, and the calibration factor
practically unchanged (1.025)
NO/NO_{x} measurements have been definitively stopped at the end of 2017.
Ground
Ozone [ug/m^{3}] ("bad ozone") Mean +/ stdev of 2023: 59.0 +/ 30.5 ug/m^{3} Mean
+/ stdev (from yearly means)


Total
Ozone Column [DU]
("good ozone") Mean and stdev of the year 2023: 320.9 +/ 40.1 DU (169 days) minimum : 225.6 (13 Feb ) maximum: 465.2 (16 Jan)
The overall trend is positive, but the trend from 2002 to 2023 (~2 decades)
is slightly negative and again positive (+0.5 DU/year) for the last 8 years.
Uccle publishes its readings on the
KNMI website (here called WebUccle), where the measuring conditions
(Direct Sun as in Diekirch, or Zenith Sky or a mixture of these) are
unknown. The second plot shows the common days for Diekirch and WebUccle,
and the calibration curve based on these readings.
The next two plots show the comparison with the Uccle
readings made by the Brewer instruments, direct sun orientation, as
published on the WOUDC. Only the data from Jan to Nov were available (8 Feb
2024); there are 126 common days available (click for larger picture). Averages and stdev of these common days::
Uccle has a slight positive trend of +3.6 DU/decade for the 19982019 period, and a similar of +3.9 DU/decade from 2010 to 2019.(see also [16])
Once more, this year measurements show how good the
Microtops II instrument is compared to the enormously more expensive
Brewers! 


CO2
mixing ratio in ppmV
Attention: The instrument for measuring CO2 (API Teledyne E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017. The jump from 2017 to 2018 seems implausible high, so a zero bias should be considered possible! Mean and stdev
of the year 2023: 441.95 +/ 8.76 ppmV Yearly CO2 means:
The 19982001 data are too unreliable to be retained
for the trend analysis. The second picture zooms on the last 6 years, and
gives the readings of Diekirch (DIK), Mauna Loa (MLO)
and Hohenpeissenberg (HPB) from 2018 to 2023; . Note the very
different elevations! Mauna Loa has no vegetation at all, Diekirch and HPB
similar grass and forests. Mauna Loa's readings have been adjusting for
latitude, by adding + 1.8 ppmV
Be careful with the Vaisala readings, as the Vaisala GPM343 might not give the same accuracy as the former API! These readings also are given for local atm. pressure and nondried air!
The CO2 data (monthly averages) show the summertime lows, which reflect the impact of variable seasonal photosynthesis (see here). A simple 12 month periodic sinus pattern was also found in 2014, 2015, 2021. Actually, as shown in addendum 3, the CO2 lowering intensity of wind speed seems to be an important modifier of this pattern, possibly masking the effect (or better: the noneffect) of photosynthesis. This happened in 2016 and 2017.
This year 2023 the yearly amplitude of the sinus fit is
3.4 ppmV (a total swing of ~6.8 ppmV, but the sinusmodel applies poorly
this year [48]). Note the large difference between the maximum September and minimum July readings!
Asymptotic CO2 mixing ratio (MassenBeck model) Addendum 3 describes our model to calculate an asymptotic CO2 mixing ratio (airspeed by cup anemometer). The plot shows how these values vary since 2018 (Vaisala sensor), suggesting a yearly increase of 4.4 ppmV since 2018 (practically the same as given by the normal readings ).

_{ .}


Air
temperature [°C]
Attention: the change of
temperature Pt100 sensor introduced a bias of +1.61, apparent since the 2017
series, and
1998 to 2023 :
10.36 +/ 0.52 °C (corrected
for bias) The
sensor location has not been moved since 2002! Sensor is a PT100
(see comments in
2015_only.xls); new 420mA
amplifier (with calibration) installed the 4th May 2016. Note the
sharp cooling in 2021 and the warming in 2022 and 2023.



Diurnal Temperature Range (DTR) [°C]
DTR = daily max  daily min temperature
Mean DTR at Diekirch &
Findel::
For 1998 to 2023: all trends are positive, the
24hmin
trend is lower than the 24hmax trend. The BEST observational data set for Luxembourg [29] stops at 2013. For our latitude of 50° North, BEST shows a positive DTR trend for the period 1988 to 2011, whereas theCMIP5 multimodel mean gives a similar but negative trend... so much for the concordance between climate models and observations! 


Winter temperatures [°C]
Values of winter DJF temperature of the year 2023: (Dec2022, Jan & Feb2023), all corrected for bias Diekirch: 3.52 Findel: 3.27 DE: 2.83 (avg. Germany) NAO: 0.64 NAO normalized index [47] The trends show warming winters since 2002 to 2022, with the warming probably caused by the NAO; the cooling for winter 2021 and warming for following winter 2022 seems to confirm this.
Trends from 2002 to 2023: The plot shows the mean temperatures from
December (of previous year) to February. It also shows in
magenta the NAO index for the months Dec to Feb (right yaxis) 

Enthalpy of moist air in kJ/kg
Mean moist enthalpy of 2023: 35.72 +/ 14.94 kJ/kg See [24] on how the energy content of moist air is
calculated. Several authors, (e.g. Prof. Roger Pielke Sr.) insist that air
temperature is a poor metric for global warming/cooling, and that the energy
content of the moist air and/or the Ocean Heat Content (OHC) are better
metrics. 


Total
Yearly Rainfall [mm]
Values of rainfall (precipitation) of the year 2023: Diekirch: 755.8 mm Findel: 887.0 mm Diekirch & Findel mean +/stdev:
1998  2023: 683.4 +/ 129.0 mm (no Findel data) Linear trends:
1998 to 2023: 1.59 mm/year
Choosing your starting point, you can find anything!
Note that the very dry year 2022 is similar to 2005 !
Clearly precipitation shows an oscillation pattern, so linear trends should be taken with precaution (or simply seen as nonsense).
A good model for the Diekirch data is a
sinus function: the calculation
(LevenbergMarquart algorithm)
suggests for the interval 2002  2023 a 5.44 years period (~65 months, R^{2} =
0.39); in the model x = 0 corresponds to 2002), with a mean value of
650 mm and an
amplitude of ~90 mm; the phase shift of 1.4 rad is close to 1/2 period. All
these values are similar to those of the preceding year. All 4
parameters are significant at the alpha = 0.95 level! The oscillatory rainfall pattern is a good example how foolish it is to apply linear regressions to data when these are harmonic, something the media, activist groups and many politicians often do without much thinking. 


Solar
energy on a horizontal plane
Values of total solar energy of
the year 2023:
1998 to 2023 mean +/ stdev: 1127.0 +/ 56.8 kWh*m^{2}*y^{1} 

Sunshine hours (meteoLCD values derived from pyranometer data by Olivieri's method) Values of sunshine hours of the year 2023:
Trends: Note the recent sharp declines in 2021 and 2023.
See paper
[23] by F. Massen
comparing 4 different methods to compute sunshine duration from pyranometer
The 2nd graph shows the plots of the four abovementioned stations. It should be noted that meteoLCD (Diekirch) is located in a valley, Trier and Findel & Maastricht airports on top of a plateau. The Findel totals are much higher than those of the other stations, which certainly is also partially caused by the use of the CampbellStokes instrument known to give too high readings. All 4 stations give totals that practically always vary in the same manner (synchronous increase and decrease). The trends of all 4 stations are positive since 1998 and similar, except Diekirch being lower All stations show distinct decline in sunshine hours in 2021, and a 2022 maximum close to that of 2003. These positive trends from 1998 on probably suffice to explain the warming since 1998. The correlations between mean temperature are yearly sunshine hours are positive for the 4 stations, and these correlations are statistically significant at the alpha = 0.95 level for all stations: Diekirch: 0.48, Findel 0.60, Trier 0.58, Maastricht 0.49 


Biologically
eff. UVB dose on a horizontal plane in eff.kWh/(m^{2}*y^{)}
Erythemal UVB dose of the year 2023: 0.143 eff. kWh/m^{2 } mean +/ stdev: 1998 to 2023: 0.134 +/ 0.010 eff. kWh*m^{2}y^{1} 2002 to 2023: 0.135 +/ 0.008 The trend over
1998  2023 is positive, in concordance with solar irradiance and sunshine
hours. . See
[10]
and [22] (poster finds slight
positive trend in June (+2%) and negative trend in August (1%), no trend
for other months, for period 1991 to 2008. 

UVA
dose on a horizontal plane in kWh/(m^{2}*y) UVA dose of the year 2023: 54.3 KWh/m^{2 } Some intermittent problems with internal temperature stabilization of the sensor; the influence seems minimal, so all readings have been kept.
mean +/ stdev: ^{ The 3 independent measurements of solar energy, eff. UVB and UVA doses all point to an increase since 1998. } 




NO_{x},
NO and NO_{2}
concentration in ug/m^{3
(End of measurements useable for trends in 2013. Measurements stopped in
2017).} Attention: only
78% of possible measurements available due to sensor downtime!
see [11] which gives ~30% reduction from 1990 to 2005 for the EU15 countries. 
References:
Addendum
1 2014 update! 
Lindzen & Choi [19] define the nonfeedback
climate sensitivity as ΔT_{0} = G_{0}*ΔF, where G_{0
}= 0.25 Wm^{2} and ΔF is the change in radiative forcing.
A change in solar irradiance of 0.82 kWh*m^{2}y^{1 } (decade 2005 to
2014) corresponds to ΔF
=  820/8760 = 0.09 Wm^{2 }and should yield a cooling of ΔT_{0
}= 0.25*0.09 = 0.02 K (or °C).per year. The meteoLCD measurements
give a cooling of 0.0057 Ky^{1}, about 3 times less. Scafetta [20] defines a climate sensitivity in respect to changes in solar radiation by k_{1s} = ΔT/ΔF and finds k_{1s }= 0.053. Our data for the decade 2005 to 2014 give ΔT/ΔF=  0.0057/(0.09) = 0.06, a value close to that of Scafetta!. Summary for the 2005 to 2014 decade: <\table>
^{ } 
Addendum 2 2018 update!

It makes for an interesting exercise to compare
the influence of mean yearly solar forcing on moist enthalpy and air
temperature for the 17 years period 2002 to 2018.
Both air temperature and moist enthalpy are positively correlated to changes in solar forcing ( = mean solar irradiance). The Pearson correlation between mean solar irradiance and moist enthalpy is 0.73 and is significant at the p = 0.05 level, whereas the correlation between mean solar irradiance and temperature is 0.42 (not significant). A change of 1 Wm^{2} of mean solar irradiance would cause a (big!) average heating of 0.5 °C per decade and a change of 0.9 kJ/kg of moist enthalpy per decade. Possibly taking into account some lag (as for instance 4 months for temperature lagging solar forcing) would change these numbers. Our temperature measurements give a heating of 0.7°C/decade for the same period (Findel shows 0.5°/decade), which is close to the correlation given if the solar irradiance was the unique warming influence!

Addendum
3 
A short
analysis of the seasonal CO2 pattern in 2014. The mean monthly CO2 data show an oscillatory pattern which can be modeled by a 6 month period sine wave. This is not consistent with the commonly admitted explication that the summer lows and winter highs are a fingerprint of changing photosynthesis, which should lead to a single annual sinus wave (as in 2013). The 6 month period is essentially caused by the low Jan, Feb and Dec values, and is replaced by the usual 12 period if these months are omitted. The right figure shows the monthly mean CO2 and monthly mean wind speeds. Clearly low wind goes with high CO2, independent of the seasons (significant correlation R = 0.86 !) The next figure gives the CO2 mixing ratios versus the monthly mean wind speed; the usual exponential model beautifully describes this pattern. The horizontal asymptote of 395.5 ppmV should correspond to the background CO2 level, as shown in [21]. There is some debate about the (global) changes of the seasonal CO2 amplitude, which seems to increase due to global greening [41], agricultural green revolution [43], changing air transcontinental circulation [42] and possibly other unknown factors. Look also at the presentation [44]. Locally it seems that the effects of higher/lower wind speeds and photosynthesis are difficult to untangle. If we restrict our data to those days where the mean wind speed is less than 1, the correlation between CO2 and wind speed is lower (0.76) but still significant. Curiously all the papers studying this seasonal amplitude problem seem to ignore the influence of changing wind speeds.

The same
analysis for 2015 Here again higher wind speeds usually go together with lower CO2 levels (notice the exception on March!), but the monthly mean values do not follow the usual model well.
If we take all 17520 individual measurements, the picture becomes clearer, and we find that our "bumerang" model follows reasonably well the overall pattern. The horizontal asymptote suggest a background CO2 level of about 389 ppmV, which seems a bit low.


The same
analysis for 2016 (wind speed from cup anemometer) The high wind speeds lower the January , February (and December) values which normally should be higher; so the "usual" sinus pattern with a trough during the summer months is mostly absent.
Using all CO2 measurements of the year, we find again our boomerang pattern; the usual model has a better R2 than in 2015, but the asymptotic value of 383 is definitively too low!


CO2
versus wind speed for 2017 (wind speed by cup anemometer): The Mauna Loa average CO2 mixing ratio for 2017 is 406.6, which would suggest that our asymptotic value of 392.3 is too low. If we use only the measurements by the new Vaisala GMP343 sensor, the asymptotic value becomes 395.7.


CO2
versus wind speed for 2018 (wind speed by cup anemometer, 17520 data
points): The Mauna Loa average CO2 mixing ratio for 2018 is 408.5, so our asymptotic value of 406 is quite close. The R^{2} of the model (the goodness of the fit) is also quite acceptable: R^{2} = 0.50. All parameters are significant at the 5% level (alpha = 0.95).


CO2
versus wind speed for 2019 (wind speed by cup anemometer, 17520 data
points): The Mauna Loa average CO2 mixing ratio for 2019 is 411.44, so our asymptotic value of 411 is practically the same! The R^{2} of the model (the goodness of the fit) is also quite acceptable: R^{2} = 0.52. All parameters are significant at the 5% level (alpha = 0.95).


CO2
versus wind speed for 2020 (wind speed by cup anemometer, 17568 data
points):
The Mauna Loa average CO2 mixing ratio for 2020 is
414, so our asymptotic value of 417 is very close, keeping in mind
that CO2 levels increase slightly with latitude! CO2 versus wind speed for 2021 (wind speed by cup anemometer, 17568 data points):
The Mauna Loa average CO2 mixing ratio for 2021 is
416.5, so our asymptotic value of 422.6 is close, keeping in mind
that CO2 levels increase slightly with latitude!
The December 2020 CO2 readings at the
Hohenpeissenberg station near Munich were 420.9 ppmV
CO2 versus wind speed for 2022 (wind speed by cup anemometer, 17560 data points):
The Mauna Loa average CO2 mixing ratio for 2022 is
418.6, so our asymptotic value of 427.3 is close, keeping in mind
that CO2 levels increase slightly with latitude! The R^{2} of the model (the goodness of the fit) is also quite acceptable: R^{2} = 0.50. All parameters are significant at the 5% level (alpha = 0.95).
CO2 versus wind speed for 2023 (wind speed by cup anemometer, 17560 data points):

file: meteolcd_trends.html
History:
12 Jan 2023: Start update of 2021 trends page to 2022
29 Mar 2023: Trends analysis for 2022 finished